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  • br Conflicts of interest br Acknowledgements This work was


    Conflicts of interest
    Acknowledgements This work was supported by funding from Young Sanjin Scholars Distinguished Professor, the Aid Program for Innovation Research Team in Shanxi Agricultural University (CXTD201201), and the Special Fund for Agro-scientific Research in the Public Interest of China (No. 201303119).
    Introduction Cyclin Dependent Kinase 4 (CDK4) is a member of Serine-Threonine kinase family and performs a crucial role in the progression of PNU-120596 [1,2]as well as in many oncogenic transformation process. Pairing of Cyclin D1 with its catalytic subunit induces a conformational change in CDK4 revealing its active site. pRb phosphorylation occurs at the active site of the holoenzyme thus formed. Phosphorylated pRb releases E2Fs which facilitates transcription and cell cycle progression into S phase [3]. Deregulation of Cyclin D1-pRb pathway has been identified in many cancers and hence expresses a great potential for therapeutic involvement [4]. Uncontrolled activity of CDK4 is associated with various types of cancers [[5], [6], [7]]. This apprehension has driven the quest for small molecules that can selectively inhibit CDK4. Several small CDK4 inhibitors molecules are under clinical trials. Recently FDA (Food and Drug Administration) approved three dual CDK4 and CDK6 selective inhibitors. These proteins are take part in the cell cycle progression of upregulated cancer cell growth. The FDA approved molecules currently available in market are Abemaciclib (LY2835219) [8], Palbociclib (P0332991) [9,10] and Ribociclib (LEE011) [11,12], (Fig. 1). These three compounds are having N-(pyridin-2-yl) pyrimidin-2-amine structural moiety in common. Hence, these class of 230 molecules along with their biological activity (pIC50 = -logIC50) were selected for analyzing the structure and activity relationship and to derive robust model from the findings. Quantitative Structure Activity Relationship (QSAR) study is intended to derive a novel statistical model which correlates various physico-chemical properties or descriptors of a set of molecules which binds to the identical site of a same protein with their biological activities [13]. The model should satisfy fitness, predictivity and robustness. According to OECD principles, the building up of a novel QSAR model comprises of selection of molecules with experimental activity assayed by same experimental methods and conditions, descriptor generation based on different structural features of molecules, selection of useful descriptors from a large descriptor data, selection of training set of molecules for model development, different regression or classification based or machine learning methods for the development of models, internal validation of model (LOO, LMO, Y-randomization [14,15], etc.), external validation using a set of molecules that were excluded in the model building procedure (Q2), domain of applicability of the proposed model identification of structural fundamentals and prediction of activity of the molecules [[14], [15], [16], [17], [18], [19], [20]]. Owing to this, the objectives of the present study are to develop noble 2D as well as 3D QSAR models from a set of Cyclin Dependent Kinase 4(CDK4) inhibitors with pyrido[2,3-d]pyramidine moiety, to understand various types of interactions of ligand molecules with receptor protein through molecular docking studies and to explain the stability of the ligand receptor complex by Binding Energy calculations.
    Results and discussion
    Conclusion With an intention to develop a persuasive model for the screening of potent molecules as Cyclin dependent kinase inhibitors, 2D QSAR studies using CANVAS and 3D QSAR studies were carried out on 230 CDK4 inhibitor molecules with pyrido [2,3-d]pyrimidine moiety. By using CANVAS, several statistical analysis was carried out using both fingerprints and molecular descriptors. It is found that kernel based partial least square analysis fitting with fingerprints using 15th split of the learning set into a test set and training set created a worthy model, among which the one with molprint2D fingerprints generated a noble model with a score value of 0.8322. External validation of the molecules were also showed a high degree of correlation with observed biological activity. Predictive power of the model is tested with test set 2 and also with Decoy set of 75 molecules. As a result we got above 0.8 correlations with the kpls_molprint_2D model. But more effective predictions are obtained by using the consensus model generated by combining kernel based partial least square models of 2D molprint, linear and radial fingerprints (correlation is 0.92). An atom based 3D QSAR model was generated from the same sets of molecules. Atomic effects of each molecule for the activity were also realized. Number of PLS factors are restricted to 6. A highly predictive model with R 2 = 0.8372 and Q2 = 0.7381 was obtained. The usefulness of the model was understood by its cross-validated R2 value which is found to be 0.5943 and Y scrambling (0.4470). From the study, it is noticed that presence of hydrophobic or nonpolar region along with electron withdrawing features in specified areas stimulate inhibitory activity of molecules. Docking experiments of these molecules in the extra precision mode with ligand flexibility followed by the binding energy determinations shed light on the leading non-covalent interactions causative of the inhibitory activity of CDK4 inhibitors. From the study, it is found that hydrogen bonding interactions with hinge region residue Val96, and residues Asp99, Lys35 and Thr102, salt bridge formation with Asp99 and л-л stacking interaction with His95 and Phe93 are common in active molecules. This л-л stacking interactions are found in the hydrophobic or non-polar region of the ligand molecules. Presence of carbonyl group in the hydrophobic area has a tendency to reduce the inhibitory activity. In molecule 187, salt bridge formation is observed in the electron withdrawing area. This area is also specified by the positive ionic features. The stability of the protein-ligand complex can be explained by calculating the decrease in binding energy after docking. The major factors that were influencing the stability of protein-ligand complex are the van der Waals interactions, lipophilic interactions and coulombic interactions. Thus, the consensus prediction of molecules using 2D QSAR models developed though kernel-based partial least square method using fingerprints besides the atom based 3DQSAR model is highly effective for screening a set of molecules with various scaffolds.